In this research, the tools and techniques of artificial intelligence were studied and employed in software engineering. And that was conducted through using the Particle Swarm Optimization PSO and Cat Swarm Optimization CSO in generating optimal test cases of the software written with C++ language in an automatic way because that enables the corporation which develops the program to save time and costs as well as ensuring the test process quality, which is estimated by 50% of the product cost. In this research, the software engineering tool Generate Test Suite GTS TOOL was constructed and modeled with the aid of the computer, which is used to generate optimal test cases automatically and this tool also support the drawing of the control flowgraphs and paths inside the program and tests each path using CSO and PSO. The proposed tool succeeded in generating optimal test cases for several programs and in a very short time. The average of generating the test cases using PSO was 4 minutes and 1.2 minutes for CSO. Where the performance of the CSO was much better than the performance of PSO.

In this research, the bees swarm intelligence was studied to appointment it to serve software engineering. And that was performed through using Artificial Bees Colony ABC Algorithm in selection of test cases for the software written by C++ language in an automatic way since to enable the corporation which develops the software to save time, effort and costs that required for testing phase and regression testing activity, which is always evaluated by 50% of the product cost. The proposed work can reduce test cases that are used in the tests of software and in regression testing activity ,also will make prioritization to the test cases, that are produced by the best selection process, by using Greedy Algorithm and Genetic Algorithm. the proposed work was applied practically on some programsthat differ in number of lines of code-.the result that appeared reduce number of test cases and make test cases in certain ordering that assists testing and regression testing for the software in safe mode and short time .

As a result of the development in multimedia technology and direct dealing with it in social media, it has led to interest in the techniques of compacting color images because of their importance at present. Since image compression enables the representation of color image data with the fewest number of bits, which reduces transmission time in the network and increases transmission speed. To ensure the compression process is performed without loss of data, the lossless compression methods are used because no data is lost during the compression process. In this research, a new system was presented to compress the color images with efficiency and high quality. Where the swarm intelligent methods were used, as well as hybridizing it with fuzzy using the Gustafson kessel fuzzy method to improve the clustering process and create new clustering methods with fuzzy swarm intelligence to obtain the best results. Swarm algorithms were used to perform the process of clustering the image data to be compressed and then obtaining a clustered data for this image data. In contrast, a lossless compression method was used to perform the encoding of this clustered data where the huffman method was used for encoding. Four methods were applied in this research to different color and lighting images. The PSO swarm intelligent was used, which in turn was hybridized with the Gustafson kessel fuzzy method to produce a new method for fuzzy particle swarm (FPSO), as well as the grey wolf optimization method GWO, which was hybridized with Gustafson kessel and obtained a new method, which is the fuzzy grey wolf optimizer FGWO, and the results were graded efficiently from the first to the fourth method, where the FGWO method with the huffman was the most efficient depending on the standards measurement that were calculated for all methods, the compression ratio was high in this new method, in addition to the standards of MSE, RMSE, PSNR, etc. among the important measurements of the compressing process.

Estimating models in software engineering are used to estimate some important and future characteristics of the software project, such as estimating the developed project effort, and that failure in the program is mainly due to wrong project management practices, so estimating the software effort is a very important step in the software management process for large projects. In this research, a software estimation tool was built to find an efficient and accurate method for estimating the effort. The average COnstructive COst MOdel COCOMO was used, which is classified as one of the best traditional methods among arithmetic estimation models. Four methods of swarm intelligence were used, the first of which is the Glowing worm Swarm Optimization GSO method and the second is the Bird Swarm Algorithm BSA and the third is the first proposed method hybrid BSA-GSO Method1 BSA-GSOM1, where the GSO and BSA algorithms were hybridized, and the performance of the third method was improved to form the fourth method, represented by the second hybrid new method, which called hybrid BSA-GSO Method2 BSA-GSOM2. The new tool was implemented with all its methods on the NASA data set and satisfactory results were obtained by the first and second swarms intelligence, and excellent results were obtained in the first proposed method, but the results of the second proposed method were better and more accurate than the previous ones. many measurements of performance were used for all the methods, the second proposed method yielded the best results from everyone.

<span lang="EN-US">The goal of the testing process is to find errors and defects in the software being developed so that they can be fixed and corrected before they are delivered to the customer. Regression testing is an essential quality testing technique during the maintenance phase of the program as it is performed to ensure the integrity of the program after modifications have been made. With the development of the software, the test suite becomes too large to be fully implemented within the given test cost in terms of budget and time. Therefore, the cost of regression testing using different techniques should be reduced, here we dealt many methods such as retest all technique, regression test selection technique (RTS) and test case prioritization technique (TCP). The efficiency of these techniques is evaluated through the use of many metrics such as average percentage of fault detected (APFD), average percentage block coverage (APBC) and average percentage decision coverage (APDC). In this paper we dealt with these different techniques used in test case selection and test case prioritization and the metrics used to evaluate their efficiency by using different techniques of artificial intelligent and describe the best of all.</span>